knn_test | R Documentation |
knn_test allows assessing the final DEGs through a machine learning step by using k-NN with a test dataset. An optimization of the k neighbours is done at the start of the process.
knn_test(train, labelsTrain, test, labelsTest, vars_selected, bestK)
train |
The train parameter is an expression matrix or data.frame that contains the train dataset with the genes in the columns and the samples in the rows. |
labelsTrain |
A vector or factor that contains the train labels for each of the samples in the train object. |
test |
The test parameter is an expression matrix or data.frame that contains the test dataset with the genes in the columns and the samples in the rows. |
labelsTest |
A vector or factor that contains the test labels for each of the samples in the test object. |
vars_selected |
The genes selected to classify by using them. It can be the final DEGs extracted with the function |
bestK |
Best K selected during the training phase. |
A list that contains six objects. The confusion matrix for each fold, the accuracy, the sensitivity, the specificity and the F1-Scores for each gene, and the predictions made.
dir <- system.file("extdata", package="KnowSeq") load(paste(dir,"/expressionExample.RData",sep = "")) trainingMatrix <- t(DEGsMatrix)[c(1:4,6:9),] trainingLabels <- labels[c(1:4,6:9)] testMatrix <- t(DEGsMatrix)[c(5,10),] testLabels <- labels[c(5,10)] bestK <- 3 # the one that has been selected results_test_knn <- knn_test(trainingMatrix, trainingLabels, testMatrix, testLabels, rownames(DEGsMatrix)[1:10], bestK)
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